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9,302 results for “DETECT”
New Outlooks on Backdoor Attacks for Fake Speech Detection
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Detecting Features of Antibody Structure through their Mediator-Accessible Redox Activities
<p>There are the compiled csv data per figure. </p>
scCDC: a computational method for gene-specific contamination detection and correction in single-cell and single-nucleus RNA-seq data
<p><span>In droplet-based single-cell and single-nucleus RNA-seq assays, systematic contamination of ambient RNA molecules biases the quantification of gene expression levels. Existing methods correct the contamination for all genes globally. However, specific evaluation for different contamination levels is lacking. Here, we show that DecontX and CellBender under-correct highly-contaminating genes, while SoupX and scAR over-correct lowly-/non-contaminating genes. Here, we develop scCDC as the first method to detect the contamination-causing genes and only correct expression levels of these genes, some of which are cell-type markers. Compared with existing decontamination methods, scCDC excels in decontaminating highly-contaminating genes while avoiding over-correction of other genes.</span></p>
EAD: Effortless Anomalies Detection, A deep learning based approach for detecting outliers in textual data
<p>Xiuzhe Wang used this data set for his project</p>
SAR Ship Detection
<p>you can use the file.</p>
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - Hand Screw Clamp Part 2
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Data for Single-molecule Detection of Modified Amino Acid Regulating Transcriptional Activity
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Figure 2 in Detection and characterization of zoonotic Bartonella spp. in rodents and shrews ectoparasites from Kigoma and Morogoro regions, Tanzania
Figure 2: Map of Kilosa and Morogoro rural Districts in Morogoro region. Source: Authors.
Figure 1 in Detection and characterization of zoonotic Bartonella spp. in rodents and shrews ectoparasites from Kigoma and Morogoro regions, Tanzania
Figure 1: Map of Kakonko and Kibondo Districts in Kigoma region. Source: Authors.
Linked collectors and determiners for: First detection of the adventive large rove beetle Ocypus nitens (Schrank) in Canada and an update of its Nearctic distribution using data generated by the public.
Natural history specimen data linked to collectors and determiners held within, "First detection of the adventive large rove beetle Ocypus nitens (Schrank) in Canada and an update of its Nearctic distribution using data generated by the public". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/e5e13b4c-e903-4507-9f00-47d764c2fe16">https://bionomia.net/dataset/e5e13b4c-e903-4507-9f00-47d764c2fe16</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/e5e13b4c-e903-4507-9f00-47d764c2fe16">https://gbif.org/dataset/e5e13b4c-e903-4507-9f00-47d764c2fe16</a>. Formatted as a Frictionless Data package.
WildDESED: An LLM-Powered Dataset for Wild Domestic Environment Sound Event Detection System
<p>A new large language model (LLM)-powered dataset namely wild domestic environment sound event detection (WildDESED). It is crafted as an extension to the original DESED dataset to reflect diverse acoustic variability and complex noises in home settings. We leveraged LLMs to generate eight different domestic scenarios based on target sound categories of the DESED dataset. Then we enriched the scenarios with a carefully tailored mixture of noises selected from AudioSet and ensured no overlap with target sound. We consider widely popular convolutional neural recurrent network to study WildDESED dataset, which depicts its challenging nature. We then apply curriculum learning by gradually increasing noise complexity to enhance the model's generalization capabilities across various noise levels.</p>
Tomato-leaf-disease-detection
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Deep learning-assisted single-atom detection of copper ions by combining click chemistry and fast scan voltammetry
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(Artifact) Understanding Model Weaknesses: A Path to Strengthening DNN-Based Android Malware Detection
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Filming the sound: Anomaly Detection on Audio Tape Recordings using Computer Vision Algorithms
<p>This repository makes available the dataset related to the paper:</p> <p>Zafer Çınar, Alessandro Russo, Matteo Spanio, Niccolò Pretto, and Sergio Canazza, <em>Filming the Sound: Anomaly Detection on Audio Tape Recordings using Computer Vision Algorithms</em>, IAI4CH, Bozen, 2024.</p> <p>The dataset and the experiment are described in the publication above.</p> <p>This repository contains two main directories (<strong>bold</strong> indicates directory names):</p> <ul> <li><strong>video samples</strong>: the actual videos used in the paper's experiment. This folder contains four subdirectories - 3.75 ips, 7.5 ips, 15 ips, and 30 ips - each representing a different playback speed (in inches per second). Within each subdirectory are several MP4 files, recorded on an A810 Studer open reel recorder, documenting the playback of magnetic audio tapes. The files follow the naming convention “Xips (Y).mp4,” where <em>X</em> represents the tape playback speed and <em>Y</em> is a serial number identifier for each video.</li> <li><strong>irregularities</strong>: the metadata for each video with timestamp and type of irregularity. The folder includes four CSV files - 3.75.csv, 7.5.csv, 15.csv, and 30.csv - corresponding to the playback speeds of the video samples. Each CSV file provides handmade annotations for its respective videos, with three columns: <ul> <li><em>video_id</em>: name of the video file in the format “Xips (Y).mp4,” where <em>X</em> is the tape speed and <em>Y</em> is the ID number.</li> <li><em>time_label</em>: timestamp indicating the irregularity, formatted as HH:MM:SS.mls.</li> <li><em>irregularity_type</em>: category of the detected anomaly, which may be one of the following: “splice,” “shadow,” “end-of-tape,” or “annotation.”</li> </ul> </li> </ul>
Gravity rate of change from slow tectonics and its detectability by MAGIC mission (Dataset)
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Soybeans-Tempeh-Image-Dataset-for-Tempeh-Maturity-Detection
Repository for Soybeans Tempeh Image Dataset for Tempeh Maturity Detection
Data from: A lateral flow strip based on gold nanoparticles to detect 6-monoacetylmorphine in oral fluid
We used lateral flow strips (LFS) based on gold nanoparticles (AuNPs) to detect 6-monoacetylmorphine (6-MAM; heroin's unique metabolite) in oral fluid samples. In this competitive lateral chromatographic immunoassay, the 6-MAM was chemically synthesized and conjugated to bovine serum albumin (BSA). The results were qualitatively detected via the color change of the test line. By using a proper sample pad, a suitable NC membrane and a customized sponge device adsorbed the oral fluid directly from mouth, and total test time was 3 minutes. The sensitivity of the assay was 4.0 ng ml-1 without any cross-reactivity with ten normal drugs, which are widely subject to abuse, including morphine and codeine. This test could be easily used on site to detect heroin in oral fluid, and it could be a promising product in the future including for driving under the influence.
Data from: The flashlight fish Anomalops katoptron uses bioluminescent light to detect prey in the dark
Bioluminescence is a fascinating phenomenon occurring in numerous animal taxa in the ocean. The reef dwelling splitfin flashlight fish (Anomalops katoptron) can be found in large schools during moonless nights in the shallow water of coral reefs and in the open surrounding water. Anomalops katoptron produce striking blink patterns with symbiotic bacteria in their sub-ocular light organs. We examined the blink frequency in A. katoptron under various laboratory conditions. During the night A. katoptron swims in schools roughly parallel to their conspecifics and display high blink frequencies of approximately 90 blinks/minute with equal on and off times. However, when planktonic prey was detected in the experimental tank, the open time increased compared to open times in the absence of prey and the frequency decreased to 20% compared to blink frequency at night in the absence of planktonic prey. During the day when the school is in a cave in the reef tank the blink frequency decreases to approximately 9 blinks/minute with increasing off-times of the light organ. Surprisingly the non-luminescent A. katoptron with non-functional light organs displayed the same blink frequencies and light organ open/closed times during the night and day as their luminescent conspecifics. In the presence of plankton non-luminescent specimens showed no change in the blink frequency and open/closed times compared to luminescent A. katoptron. Our experiments performed in a coral reef tank show that A. katoptron use bioluminescent illumination to detect planktonic prey and that the blink frequency of A. katoptron light organs follow an exogenous control by the ambient light.
Data from: Detecting epistasis from an ensemble of adapting populations
The role that epistasis plays during adaptation remains an outstanding problem, which has received considerable attention in recent years. Most of the recent empirical studies are based on ensembles of replicate populations that adapt in a fixed, laboratory controlled condition. Researchers often seek to infer the presence and form of epistasis in the fitness landscape from the time-evolution of various statistics averaged across the ensemble of populations. Here we provide a rigorous analysis of what quantities, drawn from time-series of such ensembles, can be used to infer epistasis for populations evolving under weak mutation on finite-site fitness landscapes. First we analyze the mean fitness trajectory—that is, the time course of the ensemble average fitness. We show that for any epistatic fitness landscape and starting genotype, there always exists a non-epistatic fitness landscape that produces the exact same mean fitness trajectory. Thus, the presence of epistasis is not identifiable from the mean fitness trajectory. By contrast, we show that two other ensemble statistics—the time evolution of the fitness variance across populations, and the time evolution of the mean number of substitutions—can detect certain forms of epistasis in the underlying fitness landscape.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.